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Block compressive sensing-based image denoising framework using optimized sensing matrix and split Bregman algorithm. [PDF]

Sci Rep
Thomas EN, Theeda P, Praveen T.
europepmc   +1 more source

Foreword to the CommDat special virtual issue on the raw diffraction data workshop and microsymposia at IUCr2023. [PDF]

IUCrdata
Kroon-Batenburg LMJ.
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The Compact Dual Ion Composition Experiment (CoDICE) for the IMAP Mission. [PDF]

Space Sci Rev
Livi SA, Desai MI, Ogasawara K, Kistler L, Möbius E, Starkey MJ, Dayeh MA, Kataria D, Elliott HA, Hart S, Alterman B, McComas DJ, Pollock CJ, Mukherjee J, Trevino J, Ferrell S, Brenner T, Pope SE, Tapley M, Pavlas K, Rankin JS, Shen MM, Khoo L, Alimaganbetov M, Teifert J, Roemer EM, Shaw-Lecerf M, Escobar J, Christian ER, Schwadron NA, Gkioulidou M, Grubbs G, Brysch M, Epperly M, George D, Dunn G, Ford J, Schwendeman CL, Commons M, Persyn S, Jaskulek S, Weidner S, Bert C, Ferris M, Rodriguez B, Nunez C, Urdiales C, Cortinas S, Rodriguez H.   +48 more
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High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data

IEEE Transactions on Big Data, 2023
Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the data volume, it is common for floating-point compressors to be employed ...
Tong Liu, Jinzhen Wang, Qing Liu, Shakeel Alibhai, Tao Lu, Xubin He   +5 more
semanticscholar   +1 more source

cuSZp: An Ultra-Fast GPU Error-Bounded Lossy Compression Framework with Optimized End-to-End Performance

International Conference for High Performance Computing, Networking, Storage and Analysis, 2023
Modern scientific applications and supercomputing systems are generating large amounts of data in various fields, leading to critical challenges in data storage footprints and communication times.
Yafan Huang, S. Di, Xiaodong Yu, Guanpeng Li, Franck Cappello   +4 more
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AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications

International Conference for High Performance Computing, Networking, Storage and Analysis, 2023
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an effective
Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, Sian Jin, Houjun Tang, Jean M. Sexton, S. Di, Z. Luki'c, Kai Zhao, Bo Fang, F. Cappello, J. Ahrens, Dingwen Tao   +13 more
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An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression

IEEE International Parallel and Distributed Processing Symposium, 2023
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a critical bottleneck in large-scale distributed and parallel processing.
Jiajun Huang, S. Di, Xiaodong Yu, Yujia Zhai, Zhaorui Zhang, Jinyang Liu, Xiaoyi Lu, Kenneth Raffenetti, Hui Zhou, Kai Zhao, Zizhong Chen, F. Cappello, Yan-Hua Guo, R. Thakur   +13 more
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Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks

BigData Congress [Services Society], 2023
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
Jinyang Liu, S. Di, Sian Jin, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello   +6 more
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